OpenCV码源笔记——Decision Tree决策树

来自OpenCV2.3.1 sample/c/mushroom.cpp

 

1.首先读入agaricus-lepiota.data的训练样本。

   样本中第一项是e或p代表有毒或无毒的标志位；其他是特征，可以把每个样本看做一个特征向量；

   cvSeqPush( seq, el_ptr );读入序列seq中，每一项都存储一个样本即特征向量；

   之后，把特征向量与标志位分别读入CvMat* data与CvMat* reponses中

   还有一个CvMat* missing保留丢失位当前小于0位置；

 

2.训练样本

    dtree = new CvDTree;
    dtree->train( data, CV_ROW_SAMPLE, responses, 0, 0, var_type, missing,
    CvDTreeParams( 8, // max depth
    10, // min sample count 样本数小于10时，停止分裂 
    0, // regression accuracy: N/A here；回归树的限制精度
    true, // compute surrogate split, as we have missing data；；为真时，计算missing data和变量的重要性
    15, // max number of categories (use sub-optimal algorithm for larger numbers)类型上限以保证计算速度。树会以次优分裂（suboptimal split）的形式生长。只对2种取值以上的树有意义
    10, // the number of cross-validation folds；If cv_folds > 1 then prune a tree with K-fold cross-validation where K is equal to cv_folds
    true, // use 1SE rule => smaller tree；If true 修剪树. 这将使树更紧凑,更能抵抗训练数据噪声,但有点不太准确
    true, // throw away the pruned tree branches
    priors //错分类的代价我们判断的：有毒VS无毒 错误的代价比 the array of priors, the bigger p_weight, the more attention
    // to the poisonous mushrooms
    // (a mushroom will be judjed to be poisonous with bigger chance)
    ));

 

3.

double r = dtree->predict( &sample, &mask )->value;//使用predict来预测样本，结果为 CvDTreeNode结构,dtree->predict(sample,mask)->value是分类情况下的类别或回归情况下的函数估计值；

4.interactive_classification通过人工输入特征来判断。

 

#include "opencv2/core/core_c.h"
#include "opencv2/ml/ml.hpp"
#include <stdio.h>

void help()
{
	printf("\nThis program demonstrated the use of OpenCV's decision tree function for learning and predicting data\n"
		"Usage :\n"
		"./mushroom <path to agaricus-lepiota.data>\n"
		"\n"
		"The sample demonstrates how to build a decision tree for classifying mushrooms.\n"
		"It uses the sample base agaricus-lepiota.data from UCI Repository, here is the link:\n"
		"\n"
		"Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).\n"
		"UCI Repository of machine learning databases\n"
		"[http://www.ics.uci.edu/~mlearn/MLRepository.html].\n"
		"Irvine, CA: University of California, Department of Information and Computer Science.\n"
		"\n"
		"// loads the mushroom database, which is a text file, containing\n"
		"// one training sample per row, all the input variables and the output variable are categorical,\n"
		"// the values are encoded by characters.\n\n");
}

int mushroom_read_database( const char* filename, CvMat** data, CvMat** missing, CvMat** responses )
{
	const int M = 1024;
	FILE* f = fopen( filename, "rt" );
	CvMemStorage* storage;
	CvSeq* seq;
	char buf[M+2], *ptr;
	float* el_ptr;
	CvSeqReader reader;
	int i, j, var_count = 0;

	if( !f )
		return 0;

	// read the first line and determine the number of variables
	if( !fgets( buf, M, f ))
	{
		fclose(f);
		return 0;
	}

	for( ptr = buf; *ptr != '\0'; ptr++ )
		var_count += *ptr == ',';//计算每个样本的数量，每个样本一个“，”，样本数量=var_count+1;
	assert( ptr - buf == (var_count+1)*2 );

	// create temporary memory storage to store the whole database
	//把样本存入seq中，存储空间是storage；
	el_ptr = new float[var_count+1];
	storage = cvCreateMemStorage();
	seq = cvCreateSeq( 0, sizeof(*seq), (var_count+1)*sizeof(float), storage );//

	for(;;)
	{
		for( i = 0; i <= var_count; i++ )
		{
			int c = buf[i*2];
			el_ptr[i] = c == '?' ? -1.f : (float)c;
		}
		if( i != var_count+1 )
			break;
		cvSeqPush( seq, el_ptr );
		if( !fgets( buf, M, f ) || !strchr( buf, ',' ) )
			break;
	}
	fclose(f);

	// allocate the output matrices and copy the base there
	*data = cvCreateMat( seq->total, var_count, CV_32F );//行数：样本数量；列数：样本大小；
	*missing = cvCreateMat( seq->total, var_count, CV_8U );
	*responses = cvCreateMat( seq->total, 1, CV_32F );//样本标志；

	cvStartReadSeq( seq, &reader );

	for( i = 0; i < seq->total; i++ )
	{
		const float* sdata = (float*)reader.ptr + 1;
		float* ddata = data[0]->data.fl + var_count*i;
		float* dr = responses[0]->data.fl + i;
		uchar* dm = missing[0]->data.ptr + var_count*i;

		for( j = 0; j < var_count; j++ )
		{
			ddata[j] = sdata[j];
			dm[j] = sdata[j] < 0;
		}
		*dr = sdata[-1];//样本的第一个位置是标志；
		CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
	}

	cvReleaseMemStorage( &storage );
	delete el_ptr;
	return 1;
}


CvDTree* mushroom_create_dtree( const CvMat* data, const CvMat* missing,
	const CvMat* responses, float p_weight )
{
	CvDTree* dtree;
	CvMat* var_type;
	int i, hr1 = 0, hr2 = 0, p_total = 0;
	float priors[] = { 1, p_weight };

	var_type = cvCreateMat( data->cols + 1, 1, CV_8U );
	cvSet( var_type, cvScalarAll(CV_VAR_CATEGORICAL) ); // all the variables are categorical

	dtree = new CvDTree;

	dtree->train( data, CV_ROW_SAMPLE, responses, 0, 0, var_type, missing,
		CvDTreeParams( 8, // max depth
		10, // min sample count样本数小于10时，停止分裂
		0, // regression accuracy: N/A here；回归树的限制精度
		true, // compute surrogate split, as we have missing data；为真时，计算missing data和可变的重要性正确度
		15, // max number of categories (use sub-optimal algorithm for larger numbers)类型上限以保证计算速度。树会以次优分裂（suboptimal split）的形式生长。只对2种取值以上的树有意义
		10, // the number of cross-validation folds；If cv_folds > 1 then prune a tree with K-fold cross-validation 
		true, // use 1SE rule => smaller treeIf true 修剪树. 这将使树更紧凑,更能抵抗训练数据噪声,但有点不太准确
		true, // throw away the pruned tree branches
		priors // the array of priors, the bigger p_weight, the more attention
		// to the poisonous mushrooms
		// (a mushroom will be judjed to be poisonous with bigger chance)
		));

	// compute hit-rate on the training database, demonstrates predict usage.
	
	for( i = 0; i < data->rows; i++ )
	{
		CvMat sample, mask;
		cvGetRow( data, &sample, i );
		cvGetRow( missing, &mask, i );
		double r = dtree->predict( &sample, &mask )->value;//使用predict来预测样本，结果为 CvDTreeNode结构,dtree->predict(sample,mask)->value是分类情况下的类别或回归情况下的函数估计值；
		int d = fabs(r - responses->data.fl[i]) >= FLT_EPSILON;//大于阈值FLT_EPSILON被判断为误检
		if( d )
		{
			if( r != 'p' )
				hr1++;
			else
				hr2++;
		}
		p_total += responses->data.fl[i] == 'p';
	}

	printf( "Results on the training database:\n"
		"\tPoisonous mushrooms mis-predicted: %d (%g%%)\n"
		"\tFalse-alarms: %d (%g%%)\n", hr1, (double)hr1*100/p_total,
		hr2, (double)hr2*100/(data->rows - p_total) );

	cvReleaseMat( &var_type );

	return dtree;
}


static const char* var_desc[] =
{
	"cap shape (bell=b,conical=c,convex=x,flat=f)",
	"cap surface (fibrous=f,grooves=g,scaly=y,smooth=s)",
	"cap color (brown=n,buff=b,cinnamon=c,gray=g,green=r,\n\tpink=p,purple=u,red=e,white=w,yellow=y)",
	"bruises? (bruises=t,no=f)",
	"odor (almond=a,anise=l,creosote=c,fishy=y,foul=f,\n\tmusty=m,none=n,pungent=p,spicy=s)",
	"gill attachment (attached=a,descending=d,free=f,notched=n)",
	"gill spacing (close=c,crowded=w,distant=d)",
	"gill size (broad=b,narrow=n)",
	"gill color (black=k,brown=n,buff=b,chocolate=h,gray=g,\n\tgreen=r,orange=o,pink=p,purple=u,red=e,white=w,yellow=y)",
	"stalk shape (enlarging=e,tapering=t)",
	"stalk root (bulbous=b,club=c,cup=u,equal=e,rhizomorphs=z,rooted=r)",
	"stalk surface above ring (ibrous=f,scaly=y,silky=k,smooth=s)",
	"stalk surface below ring (ibrous=f,scaly=y,silky=k,smooth=s)",
	"stalk color above ring (brown=n,buff=b,cinnamon=c,gray=g,orange=o,\n\tpink=p,red=e,white=w,yellow=y)",
	"stalk color below ring (brown=n,buff=b,cinnamon=c,gray=g,orange=o,\n\tpink=p,red=e,white=w,yellow=y)",
	"veil type (partial=p,universal=u)",
	"veil color (brown=n,orange=o,white=w,yellow=y)",
	"ring number (none=n,one=o,two=t)",
	"ring type (cobwebby=c,evanescent=e,flaring=f,large=l,\n\tnone=n,pendant=p,sheathing=s,zone=z)",
	"spore print color (black=k,brown=n,buff=b,chocolate=h,green=r,\n\torange=o,purple=u,white=w,yellow=y)",
	"population (abundant=a,clustered=c,numerous=n,\n\tscattered=s,several=v,solitary=y)",
	"habitat (grasses=g,leaves=l,meadows=m,paths=p\n\turban=u,waste=w,woods=d)",
	0
};


void print_variable_importance( CvDTree* dtree, const char** var_desc )
{
	const CvMat* var_importance = dtree->get_var_importance();
	int i;
	char input[1000];

	if( !var_importance )
	{
		printf( "Error: Variable importance can not be retrieved\n" );
		return;
	}

	printf( "Print variable importance information? (y/n) " );
	scanf( "%1s", input );
	if( input[0] != 'y' && input[0] != 'Y' )
		return;

	for( i = 0; i < var_importance->cols*var_importance->rows; i++ )
	{
		double val = var_importance->data.db[i];
		if( var_desc )
		{
			char buf[100];
			int len = strchr( var_desc[i], '(' ) - var_desc[i] - 1;
			strncpy( buf, var_desc[i], len );
			buf[len] = '\0';
			printf( "%s", buf );
		}
		else
			printf( "var #%d", i );
		printf( ": %g%%\n", val*100. );
	}
}

void interactive_classification( CvDTree* dtree, const char** var_desc )
{
	char input[1000];
	const CvDTreeNode* root;
	CvDTreeTrainData* data;

	if( !dtree )
		return;

	root = dtree->get_root();
	data = dtree->get_data();

	for(;;)
	{
		const CvDTreeNode* node;

		printf( "Start/Proceed with interactive mushroom classification (y/n): " );
		scanf( "%1s", input );
		if( input[0] != 'y' && input[0] != 'Y' )
			break;
		printf( "Enter 1-letter answers, '?' for missing/unknown value...\n" ); 

		// custom version of predict
		//传统的预测方式;
		node = root;
		for(;;)
		{
			CvDTreeSplit* split = node->split;
			int dir = 0;

			if( !node->left || node->Tn <= dtree->get_pruned_tree_idx() || !node->split )
				break;

			for( ; split != 0; )
			{
				int vi = split->var_idx, j;
				int count = data->cat_count->data.i[vi];
				const int* map = data->cat_map->data.i + data->cat_ofs->data.i[vi];

				printf( "%s: ", var_desc[vi] );
				scanf( "%1s", input );

				if( input[0] == '?' )
				{
					split = split->next;
					continue;
				}

				// convert the input character to the normalized value of the variable
				for( j = 0; j < count; j++ )
					if( map[j] == input[0] )
						break;
				if( j < count )
				{
					dir = (split->subset[j>>5] & (1 << (j&31))) ? -1 : 1;
					if( split->inversed )
						dir = -dir;
					break;
				}
				else
					printf( "Error: unrecognized value\n" );
			}

			if( !dir )
			{
				printf( "Impossible to classify the sample\n");
				node = 0;
				break;
			}
			node = dir < 0 ? node->left : node->right;
		}

		if( node )
			printf( "Prediction result: the mushroom is %s\n",
			node->class_idx == 0 ? "EDIBLE" : "POISONOUS" );
		printf( "\n-----------------------------\n" );
	}
}


int main( int argc, char** argv )
{
	CvMat *data = 0, *missing = 0, *responses = 0;
	CvDTree* dtree;
	const char* base_path = argc >= 2 ? argv[1] : "agaricus-lepiota.data";

	help();

	if( !mushroom_read_database( base_path, &data, &missing, &responses ) )
	{
		printf( "\nUnable to load the training database\n\n");
		help();
		return -1;
	}

	dtree = mushroom_create_dtree( data, missing, responses,
		10 // poisonous mushrooms will have 10x higher weight in the decision tree
		);
	cvReleaseMat( &data );
	cvReleaseMat( &missing );
	cvReleaseMat( &responses );

	print_variable_importance( dtree, var_desc );
	interactive_classification( dtree, var_desc );
	delete dtree;

	return 0;
}